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| Marques de Sa J.P. — Pattern recognition: concepts, methods, and applications |
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| Предметный указатель |
Activation function 155
Acyclic graph 249
ADALINE 159
Adaptive filtering 152
Adjusted prevalences 95
Agreement table 75
Ambiguous grammar 253
Approximation error 148
Assignment problem 276
Asynchronous updating 227
Attribute difference 268
Attribute vector 248
Attributed grammar 260
Attributed graph 282
Attributed string 248
Average risk 95 96
Average squared error 184
Average vote 233
Back propagation 172
Backward search 123
batch 175
batch mode 152
Bayes classifier 91
Bayes rule 95 263
Bhattacharyya distance 100
Bias 22 150
Binary tree 27
Bootstrap method 128
Box plot 42
Branch and bound 122
CAM 227
Canonical hyperplane 216
CART method 137
Case shuffling 178
Cauchy distribution 100
Centroid 70
Chain code 245
Class symbol 251
Classes 11
Classification, risk 93
Classification, supervised 53
Classification, unsupervised 53
CLIQUE 281
Clustering 14
Co-operative network 235
Cohen's k 75
Compatibility factor 271
Compatibility matrix 278
Concatenation 247
Concept driven 11
Conjugate direction 202
Context-free grammar 256
Correlation 35
Correlation classifier 84
Cost function 217
Covariance 35
Cross-validation 74
Crossover 209
Curse of dimensionality 46 109
Data driven 13
Data, mining 138
Data, warehouse 138
decision functions 22
Decision regions 21
Decision rule 133
Decision table 135
Decision tree 130
Dendrogram 59
Derivation 252
Design set 12
Dichotomies 192
Digraph 248
Dimensionality ratio 46 178
Discrete relaxation 274 277 282
Dynamic search 123
Edge set 248
Edit costs 266
Edit operations 266 281
Edition method 115
Eigenvalues 38
Eigenvectors 38 65 121
Elitism 209
Empirical error 200
Empirical risk 97
Ensemble network 233
entropy 137
Epoch 152 175
ERM 97
Error bias 127 189
Error energy 148
Error mean 184
Error surface 150
Error test set 106
Error training set 80 106
Error variance 106 127 189
Euclidian classifier 84
Exhaustive search 122
External variables 181
Factor analysis 65
Factor loadings 65
Fat shattering 198
Feature extraction 16
Feature selection 121
Feature space 26
Feature standardization 56
Feature vector 13
features 12
FHR 260
Finite state machine 257
Fisher discriminant 88 186
Fitness factor 208
Forecasting 181
Forvvard search 123
Gaussian distribution 98
Genetic algorithm 123 208
Gradient descent 151
Graph 248
Graph, edge 248
Graph, node 248
Growth function 194
Hamming distance 228
Hard limiter 158
Hebb rule 170 228
Hessian matrix 186
Hidden neuron 167
Hierarchical classifier 130
Hierarchical network 232
histogram 42
Holdout method 128
Homomorphism 279
Hopfield netvvork 169 226 275
Hough transform 247
Hyperellisoid 85
Hyperplane 22 80
Hypersphere 30
Icicle plot 58
ID3 algorithm 137
In-degree 248
Interpolating kernel 111
Interpolation 213
Interquartile 42
IPS 175
ISODATA 70
Isomorphism 280
Jogging weights 178
K - NN 114
Kaiser criterion 40
Kernel function 213 222
Kohonen network 169 223
Kolmogorov - Smimov test 43
| Kruskal - Wallis test 44
Lagrange multipliers 217
Learning Curve 166
Learning rate 151
Learning size (bounds) 196
Least mean square 149
Leave-one-out method 128
Levenberg - Marquardt 205
Levenshtein distance 266
Likelihood 91
Lillefors correction 43
Line search 202
Linear discriminant 22 83 147
Linear network 148
Linear transformation 31
Linkage, average between groups 62
Linkage, average within groups 62
Linkage, complete 58 62
Linkage, single 60
Linkage, Ward 63
LMS 149
Local minimum 157 177
Logistic distribution 100
Mahalanobis classifier 85
Mahalanobis distance 36 99
Majority vote 233
Margin of separation 215
Match graph 280
Matching score 269 276
Max vote 233
Maximum likelihood 98
Means clustering 70
Merit criterion 121
Merit index 71
Metric 29
MLP 167
Modular network 232
Momentum factor 175
Multidimensional scaling 67
Mutation 209
Nearest neighbour 114
Network complexity 169
Network pruning 191
Network weight 149
Node impurity 136
Node set 248
Node, splitting 138
Noise adding 178
Norm, Chebychev 29
Norm, city-block 29
Norm, Euclidian 29
Norm, Mahalanobis 32
Norm, Minkowsky 30
Norm, power 29
Null string 247
Optimal hyperplane 216
Optimum classifier 99
Orthonormal matrix 38
Orthonormal transformation 37
Out-degree 248
Outlier 42
Output neuron 155 200
Parameter estimation 97
Parsing tree 252
Partition method 128
Parzen window 110
Pattern acquisition 16
Pattern classification 4
Pattern description 9
Pattern primitive 243
Pattern recognition 1
Pattern regression 7
Patterns 12
PDL 253
Perceptron 159
Perceptron rule 160
Piecewise linear 244
Pooled covariance 103
post-processing 17 176
pre-processing 17 176
Predictive modelling 235
Prevalence 90
Prevalence threshold 91
primitive 243
Principal component 39 65
Probabilistic neural network 113 212
Probabilistic relaxation 271
Production rule 251
Prototype stale 230
Pseudo dimension 198
Pseudo inverse 149 207
Pseudo shattering 197
Quadratic classifier 24 127
Quadratic decision function 23
Quadratic form 32
Radial basis functions 168 212 222
Radial layer 213
Ranking index 185
RBF 168 213
Recurrent network 169
Regular grammar 256
Regularization 191
Reject region 103
Reject threshold 104
Replication analysis 74
Resubstitution method 127
Retrieval phase 228
RMS error 184
ROC area 120
ROC curve 118
ROC threshold 119
Rule probability 261
Scaling 34
Scatter plot 42
Schema theorem 209
Scree test 41
Semantic rule 260
Sensibility 116
Sensitivity analysis 191
Separability, absolute 26
Separability, pairwise 28
Sequential search 123
Shape primitive 246
Shapiro - Wilk test 43
Shattered sets 194
Shepard diagram 68
Sigmoidal function 158
Slack variables 219
Specificity 117
Split criterion 136
SRM 201
Standardization 56
Starting symbol 251
State, diagram 257
State, spurious 228
State, stable 227
State, transition table 258
Stochastic grammar 261
storage capacity 230
Storage phase 228
Stress 67
String matching 265
Structural risk 201
Successor method 264
Supervised PR 14
Support vector machine 168
Support vectors 216
SVM 168 215
Synchronous updating 227
target value 148
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